Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations99003
Missing cells0
Missing cells (%)0.0%
Duplicate rows6
Duplicate rows (%)< 0.1%
Total size in memory11.3 MiB
Average record size in memory120.0 B

Variable types

Numeric13
DateTime1
Categorical1

Alerts

Dataset has 6 (< 0.1%) duplicate rowsDuplicates
age is highly overall correlated with dob_yearHigh correlation
dob_year is highly overall correlated with ageHigh correlation
friend_count is highly overall correlated with friendships_initiated and 3 other fieldsHigh correlation
friendships_initiated is highly overall correlated with friend_count and 2 other fieldsHigh correlation
likes is highly overall correlated with likes_received and 4 other fieldsHigh correlation
likes_received is highly overall correlated with friend_count and 5 other fieldsHigh correlation
mobile_likes is highly overall correlated with likes and 3 other fieldsHigh correlation
mobile_likes_received is highly overall correlated with friend_count and 5 other fieldsHigh correlation
www_likes is highly overall correlated with likes and 1 other fieldsHigh correlation
www_likes_received is highly overall correlated with friend_count and 5 other fieldsHigh correlation
likes_received is highly skewed (γ1 = 112.0745682) Skewed
mobile_likes_received is highly skewed (γ1 = 107.5312999) Skewed
www_likes_received is highly skewed (γ1 = 126.257317) Skewed
friend_count has 1962 (2.0%) zeros Zeros
friendships_initiated has 2997 (3.0%) zeros Zeros
likes has 22308 (22.5%) zeros Zeros
likes_received has 24428 (24.7%) zeros Zeros
mobile_likes has 35056 (35.4%) zeros Zeros
mobile_likes_received has 30003 (30.3%) zeros Zeros
www_likes has 60999 (61.6%) zeros Zeros
www_likes_received has 36864 (37.2%) zeros Zeros

Reproduction

Analysis started2025-05-18 06:26:30.040623
Analysis finished2025-05-18 06:26:56.194517
Duration26.15 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

age
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.280224
Minimum13
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2025-05-18T06:26:56.298454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15
Q120
median28
Q350
95-th percentile90
Maximum113
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation22.589748
Coefficient of variation (CV)0.60594455
Kurtosis1.5614468
Mean37.280224
Median Absolute Deviation (MAD)10
Skewness1.4152607
Sum3690854
Variance510.29673
MonotonicityNot monotonic
2025-05-18T06:26:56.436855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 5196
 
5.2%
23 4404
 
4.4%
19 4391
 
4.4%
20 3769
 
3.8%
21 3671
 
3.7%
25 3641
 
3.7%
17 3283
 
3.3%
16 3086
 
3.1%
22 3032
 
3.1%
24 2827
 
2.9%
Other values (91) 61703
62.3%
ValueCountFrequency (%)
13 484
 
0.5%
14 1925
 
1.9%
15 2618
2.6%
16 3086
3.1%
17 3283
3.3%
18 5196
5.2%
19 4391
4.4%
20 3769
3.8%
21 3671
3.7%
22 3032
3.1%
ValueCountFrequency (%)
113 202
 
0.2%
112 18
 
< 0.1%
111 18
 
< 0.1%
110 15
 
< 0.1%
109 9
 
< 0.1%
108 1661
1.7%
107 98
 
0.1%
106 125
 
0.1%
105 80
 
0.1%
104 73
 
0.1%
Distinct23151
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Memory size773.6 KiB
Minimum1900-01-01 00:00:00
Maximum2000-10-27 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-18T06:26:56.570120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:56.715446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

dob_day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.530408
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2025-05-18T06:26:56.830432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median14
Q322
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0156064
Coefficient of variation (CV)0.62046477
Kurtosis-1.1889601
Mean14.530408
Median Absolute Deviation (MAD)8
Skewness0.10784076
Sum1438554
Variance81.281158
MonotonicityNot monotonic
2025-05-18T06:26:56.940969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 7900
 
8.0%
10 4030
 
4.1%
15 3555
 
3.6%
5 3545
 
3.6%
12 3413
 
3.4%
2 3409
 
3.4%
3 3291
 
3.3%
17 3266
 
3.3%
20 3263
 
3.3%
14 3219
 
3.3%
Other values (21) 60112
60.7%
ValueCountFrequency (%)
1 7900
8.0%
2 3409
3.4%
3 3291
3.3%
4 3217
3.2%
5 3545
3.6%
6 3108
 
3.1%
7 3010
 
3.0%
8 3202
3.2%
9 3003
 
3.0%
10 4030
4.1%
ValueCountFrequency (%)
31 1507
1.5%
30 2530
2.6%
29 2508
2.5%
28 2955
3.0%
27 2755
2.8%
26 2753
2.8%
25 3217
3.2%
24 2807
2.8%
23 2864
2.9%
22 2838
2.9%

dob_year
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1975.7198
Minimum1900
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2025-05-18T06:26:57.089333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1923
Q11963
median1985
Q31993
95-th percentile1998
Maximum2000
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation22.589748
Coefficient of variation (CV)0.01143368
Kurtosis1.5614468
Mean1975.7198
Median Absolute Deviation (MAD)10
Skewness-1.4152607
Sum1.9560218 × 108
Variance510.29673
MonotonicityNot monotonic
2025-05-18T06:26:57.226794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1995 5196
 
5.2%
1990 4404
 
4.4%
1994 4391
 
4.4%
1993 3769
 
3.8%
1992 3671
 
3.7%
1988 3641
 
3.7%
1996 3283
 
3.3%
1997 3086
 
3.1%
1991 3032
 
3.1%
1989 2827
 
2.9%
Other values (91) 61703
62.3%
ValueCountFrequency (%)
1900 202
 
0.2%
1901 18
 
< 0.1%
1902 18
 
< 0.1%
1903 15
 
< 0.1%
1904 9
 
< 0.1%
1905 1661
1.7%
1906 98
 
0.1%
1907 125
 
0.1%
1908 80
 
0.1%
1909 73
 
0.1%
ValueCountFrequency (%)
2000 484
 
0.5%
1999 1925
 
1.9%
1998 2618
2.6%
1997 3086
3.1%
1996 3283
3.3%
1995 5196
5.2%
1994 4391
4.4%
1993 3769
3.8%
1992 3671
3.7%
1991 3032
3.1%

dob_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2833652
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2025-05-18T06:26:57.336619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5296716
Coefficient of variation (CV)0.5617486
Kurtosis-1.2403976
Mean6.2833652
Median Absolute Deviation (MAD)3
Skewness0.031295507
Sum622072
Variance12.458581
MonotonicityNot monotonic
2025-05-18T06:26:57.439750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 11772
11.9%
10 8476
8.6%
5 8271
8.4%
8 8266
8.3%
3 8110
8.2%
7 8021
8.1%
9 7939
8.0%
12 7894
8.0%
4 7810
7.9%
2 7632
7.7%
Other values (2) 14812
15.0%
ValueCountFrequency (%)
1 11772
11.9%
2 7632
7.7%
3 8110
8.2%
4 7810
7.9%
5 8271
8.4%
6 7607
7.7%
7 8021
8.1%
8 8266
8.3%
9 7939
8.0%
10 8476
8.6%
ValueCountFrequency (%)
12 7894
8.0%
11 7205
7.3%
10 8476
8.6%
9 7939
8.0%
8 8266
8.3%
7 8021
8.1%
6 7607
7.7%
5 8271
8.4%
4 7810
7.9%
3 8110
8.2%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size773.6 KiB
male
58749 
female
40254 

Length

Max length6
Median length4
Mean length4.8131875
Min length4

Characters and Unicode

Total characters476520
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 58749
59.3%
female 40254
40.7%

Length

2025-05-18T06:26:57.561978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T06:26:57.640939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 58749
59.3%
female 40254
40.7%

Most occurring characters

ValueCountFrequency (%)
e 139257
29.2%
m 99003
20.8%
a 99003
20.8%
l 99003
20.8%
f 40254
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 476520
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 139257
29.2%
m 99003
20.8%
a 99003
20.8%
l 99003
20.8%
f 40254
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 476520
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 139257
29.2%
m 99003
20.8%
a 99003
20.8%
l 99003
20.8%
f 40254
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 476520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 139257
29.2%
m 99003
20.8%
a 99003
20.8%
l 99003
20.8%
f 40254
 
8.4%

tenure
Real number (ℝ)

Distinct2426
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean537.88483
Minimum0
Maximum3139
Zeros70
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2025-05-18T06:26:57.734216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47
Q1226
median412
Q3675
95-th percentile1575
Maximum3139
Range3139
Interquartile range (IQR)449

Descriptive statistics

Standard deviation457.6456
Coefficient of variation (CV)0.85082451
Kurtosis2.1991817
Mean537.88483
Median Absolute Deviation (MAD)213
Skewness1.5357092
Sum53252212
Variance209439.5
MonotonicityNot monotonic
2025-05-18T06:26:57.860121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 173
 
0.2%
303 170
 
0.2%
242 164
 
0.2%
272 163
 
0.2%
257 161
 
0.2%
297 161
 
0.2%
280 160
 
0.2%
285 160
 
0.2%
278 158
 
0.2%
284 158
 
0.2%
Other values (2416) 97375
98.4%
ValueCountFrequency (%)
0 70
0.1%
1 60
0.1%
2 72
0.1%
3 79
0.1%
4 86
0.1%
5 92
0.1%
6 93
0.1%
7 84
0.1%
8 87
0.1%
9 93
0.1%
ValueCountFrequency (%)
3139 3
< 0.1%
3129 1
 
< 0.1%
3128 1
 
< 0.1%
3101 1
 
< 0.1%
3019 1
 
< 0.1%
2958 1
 
< 0.1%
2926 1
 
< 0.1%
2888 1
 
< 0.1%
2822 1
 
< 0.1%
2788 1
 
< 0.1%

friend_count
Real number (ℝ)

High correlation  Zeros 

Distinct2562
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.35079
Minimum0
Maximum4923
Zeros1962
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2025-05-18T06:26:57.993365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q131
median82
Q3206
95-th percentile720
Maximum4923
Range4923
Interquartile range (IQR)175

Descriptive statistics

Standard deviation387.30423
Coefficient of variation (CV)1.9725117
Kurtosis50.094273
Mean196.35079
Median Absolute Deviation (MAD)64
Skewness6.0590085
Sum19439317
Variance150004.57
MonotonicityNot monotonic
2025-05-18T06:26:58.150892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1962
 
2.0%
1 1816
 
1.8%
2 1117
 
1.1%
3 860
 
0.9%
5 789
 
0.8%
4 749
 
0.8%
10 737
 
0.7%
24 732
 
0.7%
6 720
 
0.7%
29 719
 
0.7%
Other values (2552) 88802
89.7%
ValueCountFrequency (%)
0 1962
2.0%
1 1816
1.8%
2 1117
1.1%
3 860
0.9%
4 749
 
0.8%
5 789
0.8%
6 720
 
0.7%
7 671
 
0.7%
8 718
 
0.7%
9 700
 
0.7%
ValueCountFrequency (%)
4923 1
< 0.1%
4917 1
< 0.1%
4863 1
< 0.1%
4845 1
< 0.1%
4844 1
< 0.1%
4826 1
< 0.1%
4817 1
< 0.1%
4803 1
< 0.1%
4797 1
< 0.1%
4794 1
< 0.1%

friendships_initiated
Real number (ℝ)

High correlation  Zeros 

Distinct1519
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.45247
Minimum0
Maximum4144
Zeros2997
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2025-05-18T06:26:58.295203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q117
median46
Q3117
95-th percentile418
Maximum4144
Range4144
Interquartile range (IQR)100

Descriptive statistics

Standard deviation188.78695
Coefficient of variation (CV)1.7569345
Kurtosis42.535601
Mean107.45247
Median Absolute Deviation (MAD)36
Skewness5.1507574
Sum10638117
Variance35640.513
MonotonicityNot monotonic
2025-05-18T06:26:58.453950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2997
 
3.0%
1 2212
 
2.2%
2 1551
 
1.6%
3 1355
 
1.4%
4 1352
 
1.4%
5 1328
 
1.3%
6 1328
 
1.3%
11 1319
 
1.3%
8 1314
 
1.3%
13 1279
 
1.3%
Other values (1509) 82968
83.8%
ValueCountFrequency (%)
0 2997
3.0%
1 2212
2.2%
2 1551
1.6%
3 1355
1.4%
4 1352
1.4%
5 1328
1.3%
6 1328
1.3%
7 1237
1.2%
8 1314
1.3%
9 1245
1.3%
ValueCountFrequency (%)
4144 1
< 0.1%
3654 1
< 0.1%
3594 1
< 0.1%
3538 1
< 0.1%
3415 1
< 0.1%
3238 1
< 0.1%
3233 1
< 0.1%
3086 1
< 0.1%
3078 1
< 0.1%
3024 1
< 0.1%

likes
Real number (ℝ)

High correlation  Zeros 

Distinct2924
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.07879
Minimum0
Maximum25111
Zeros22308
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2025-05-18T06:26:58.588100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q381
95-th percentile726
Maximum25111
Range25111
Interquartile range (IQR)80

Descriptive statistics

Standard deviation572.28068
Coefficient of variation (CV)3.6666141
Kurtosis200.44569
Mean156.07879
Median Absolute Deviation (MAD)11
Skewness11.023704
Sum15452268
Variance327505.18
MonotonicityNot monotonic
2025-05-18T06:26:58.730673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22308
22.5%
1 6928
 
7.0%
2 4434
 
4.5%
3 3240
 
3.3%
4 2507
 
2.5%
5 2027
 
2.0%
6 1806
 
1.8%
7 1618
 
1.6%
8 1430
 
1.4%
9 1381
 
1.4%
Other values (2914) 51324
51.8%
ValueCountFrequency (%)
0 22308
22.5%
1 6928
 
7.0%
2 4434
 
4.5%
3 3240
 
3.3%
4 2507
 
2.5%
5 2027
 
2.0%
6 1806
 
1.8%
7 1618
 
1.6%
8 1430
 
1.4%
9 1381
 
1.4%
ValueCountFrequency (%)
25111 1
< 0.1%
21652 1
< 0.1%
16732 1
< 0.1%
16583 1
< 0.1%
14799 1
< 0.1%
14355 1
< 0.1%
14050 1
< 0.1%
14039 1
< 0.1%
13692 1
< 0.1%
13622 1
< 0.1%

likes_received
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct2681
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.68936
Minimum0
Maximum261197
Zeros24428
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2025-05-18T06:26:58.874788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q359
95-th percentile561
Maximum261197
Range261197
Interquartile range (IQR)58

Descriptive statistics

Standard deviation1387.9196
Coefficient of variation (CV)9.7268611
Kurtosis17384.94
Mean142.68936
Median Absolute Deviation (MAD)8
Skewness112.07457
Sum14126675
Variance1926320.9
MonotonicityNot monotonic
2025-05-18T06:26:59.016424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24428
24.7%
1 7305
 
7.4%
2 4541
 
4.6%
3 3347
 
3.4%
4 2669
 
2.7%
5 2373
 
2.4%
6 1873
 
1.9%
7 1680
 
1.7%
8 1538
 
1.6%
9 1351
 
1.4%
Other values (2671) 47898
48.4%
ValueCountFrequency (%)
0 24428
24.7%
1 7305
 
7.4%
2 4541
 
4.6%
3 3347
 
3.4%
4 2669
 
2.7%
5 2373
 
2.4%
6 1873
 
1.9%
7 1680
 
1.7%
8 1538
 
1.6%
9 1351
 
1.4%
ValueCountFrequency (%)
261197 1
< 0.1%
178166 1
< 0.1%
152014 1
< 0.1%
106025 1
< 0.1%
82623 1
< 0.1%
53534 1
< 0.1%
52964 1
< 0.1%
45633 1
< 0.1%
42449 1
< 0.1%
39536 1
< 0.1%

mobile_likes
Real number (ℝ)

High correlation  Zeros 

Distinct2396
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.1163
Minimum0
Maximum25111
Zeros35056
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2025-05-18T06:26:59.167014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q346
95-th percentile481.9
Maximum25111
Range25111
Interquartile range (IQR)46

Descriptive statistics

Standard deviation445.25299
Coefficient of variation (CV)4.1958963
Kurtosis360.98858
Mean106.1163
Median Absolute Deviation (MAD)4
Skewness14.161237
Sum10505832
Variance198250.22
MonotonicityNot monotonic
2025-05-18T06:26:59.310141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35056
35.4%
1 6297
 
6.4%
2 3941
 
4.0%
3 2917
 
2.9%
4 2265
 
2.3%
5 1794
 
1.8%
6 1598
 
1.6%
7 1395
 
1.4%
8 1212
 
1.2%
9 1149
 
1.2%
Other values (2386) 41379
41.8%
ValueCountFrequency (%)
0 35056
35.4%
1 6297
 
6.4%
2 3941
 
4.0%
3 2917
 
2.9%
4 2265
 
2.3%
5 1794
 
1.8%
6 1598
 
1.6%
7 1395
 
1.4%
8 1212
 
1.2%
9 1149
 
1.2%
ValueCountFrequency (%)
25111 1
< 0.1%
21652 1
< 0.1%
16732 1
< 0.1%
14039 1
< 0.1%
13529 1
< 0.1%
12934 1
< 0.1%
12639 1
< 0.1%
12104 1
< 0.1%
12083 1
< 0.1%
11959 1
< 0.1%

mobile_likes_received
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct2004
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.120491
Minimum0
Maximum138561
Zeros30003
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2025-05-18T06:26:59.445232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q333
95-th percentile317
Maximum138561
Range138561
Interquartile range (IQR)33

Descriptive statistics

Standard deviation839.88944
Coefficient of variation (CV)9.9843621
Kurtosis15522.649
Mean84.120491
Median Absolute Deviation (MAD)4
Skewness107.5313
Sum8328181
Variance705414.28
MonotonicityNot monotonic
2025-05-18T06:26:59.577060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30003
30.3%
1 8243
 
8.3%
2 4948
 
5.0%
3 3608
 
3.6%
4 2944
 
3.0%
5 2383
 
2.4%
6 2022
 
2.0%
7 1745
 
1.8%
8 1521
 
1.5%
9 1437
 
1.5%
Other values (1994) 40149
40.6%
ValueCountFrequency (%)
0 30003
30.3%
1 8243
 
8.3%
2 4948
 
5.0%
3 3608
 
3.6%
4 2944
 
3.0%
5 2383
 
2.4%
6 2022
 
2.0%
7 1745
 
1.8%
8 1521
 
1.5%
9 1437
 
1.5%
ValueCountFrequency (%)
138561 1
< 0.1%
131244 1
< 0.1%
89911 1
< 0.1%
73333 1
< 0.1%
43410 1
< 0.1%
30754 1
< 0.1%
30387 1
< 0.1%
27353 1
< 0.1%
20770 1
< 0.1%
18925 1
< 0.1%

www_likes
Real number (ℝ)

High correlation  Zeros 

Distinct1726
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.962425
Minimum0
Maximum14865
Zeros60999
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2025-05-18T06:26:59.716244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile208
Maximum14865
Range14865
Interquartile range (IQR)7

Descriptive statistics

Standard deviation285.56015
Coefficient of variation (CV)5.7154982
Kurtosis449.14848
Mean49.962425
Median Absolute Deviation (MAD)0
Skewness16.911025
Sum4946430
Variance81544.6
MonotonicityNot monotonic
2025-05-18T06:26:59.858407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60999
61.6%
1 4697
 
4.7%
2 2760
 
2.8%
3 1948
 
2.0%
4 1419
 
1.4%
5 1202
 
1.2%
6 1081
 
1.1%
7 897
 
0.9%
8 792
 
0.8%
9 757
 
0.8%
Other values (1716) 22451
 
22.7%
ValueCountFrequency (%)
0 60999
61.6%
1 4697
 
4.7%
2 2760
 
2.8%
3 1948
 
2.0%
4 1419
 
1.4%
5 1202
 
1.2%
6 1081
 
1.1%
7 897
 
0.9%
8 792
 
0.8%
9 757
 
0.8%
ValueCountFrequency (%)
14865 1
< 0.1%
12903 1
< 0.1%
11077 1
< 0.1%
10763 1
< 0.1%
10627 1
< 0.1%
10539 1
< 0.1%
10255 1
< 0.1%
10232 1
< 0.1%
9902 1
< 0.1%
9431 1
< 0.1%

www_likes_received
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1636
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.568831
Minimum0
Maximum129953
Zeros36864
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2025-05-18T06:27:00.039609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q320
95-th percentile227
Maximum129953
Range129953
Interquartile range (IQR)20

Descriptive statistics

Standard deviation601.41635
Coefficient of variation (CV)10.268539
Kurtosis23812.249
Mean58.568831
Median Absolute Deviation (MAD)2
Skewness126.25732
Sum5798490
Variance361701.62
MonotonicityNot monotonic
2025-05-18T06:27:00.264493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36864
37.2%
1 8513
 
8.6%
2 5111
 
5.2%
3 3586
 
3.6%
4 2828
 
2.9%
5 2317
 
2.3%
6 1918
 
1.9%
7 1602
 
1.6%
8 1445
 
1.5%
9 1373
 
1.4%
Other values (1626) 33446
33.8%
ValueCountFrequency (%)
0 36864
37.2%
1 8513
 
8.6%
2 5111
 
5.2%
3 3586
 
3.6%
4 2828
 
2.9%
5 2317
 
2.3%
6 1918
 
1.9%
7 1602
 
1.6%
8 1445
 
1.5%
9 1373
 
1.4%
ValueCountFrequency (%)
129953 1
< 0.1%
62103 1
< 0.1%
39605 1
< 0.1%
39213 1
< 0.1%
34039 1
< 0.1%
32692 1
< 0.1%
29337 1
< 0.1%
23147 1
< 0.1%
22644 1
< 0.1%
15096 1
< 0.1%

Interactions

2025-05-18T06:26:54.083821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:32.003975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:33.470746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:35.283014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:37.414689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:40.146614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:41.725773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:43.225356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:44.743933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:46.601223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:48.509130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:50.463680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:52.062593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:54.192443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:32.106901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-18T06:26:35.447334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:37.520458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:40.267769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-18T06:26:43.344080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-18T06:26:37.642169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-18T06:26:46.821255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:48.830854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-18T06:26:51.552702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:53.587853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:55.244087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:33.114673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:34.722770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:36.953234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:39.788255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:41.359420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:42.867305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:44.375435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:46.225784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:47.988026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:50.105621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:51.680075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:53.707320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:55.377000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:33.239080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:34.912388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:37.153492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:39.909962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:41.483908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:42.989279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:44.511537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:46.347745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:48.170724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:50.224228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:51.803771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:53.832791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:55.519696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:33.358961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:35.101270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:37.294146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:40.031345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:41.610087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:43.104956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:44.631320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:46.466807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:48.335614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:50.348139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:51.946082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T06:26:53.963475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-18T06:27:01.098754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agedob_daydob_monthdob_yearfriend_countfriendships_initiatedgenderlikeslikes_receivedmobile_likesmobile_likes_receivedtenurewww_likeswww_likes_received
age1.0000.0340.029-1.000-0.162-0.1820.1340.0360.024-0.0690.0060.3410.0780.041
dob_day0.0341.0000.136-0.0340.0510.0440.0510.0430.0440.0300.0400.0530.0370.044
dob_month0.0290.1361.000-0.0290.0420.0370.0460.0310.0380.0260.0380.0360.0250.036
dob_year-1.000-0.034-0.0291.0000.1620.1820.136-0.036-0.0240.069-0.006-0.341-0.078-0.041
friend_count-0.1620.0510.0420.1621.0000.9460.0840.4680.5530.4360.5480.3090.2730.507
friendships_initiated-0.1820.0440.0370.1820.9461.0000.0190.4490.5150.4190.5100.2300.2600.470
gender0.1340.0510.0460.1360.0840.0191.0000.0600.0090.0430.0060.0880.0520.005
likes0.0360.0430.031-0.0360.4680.4490.0601.0000.8090.8340.7840.1420.5480.755
likes_received0.0240.0440.038-0.0240.5530.5150.0090.8091.0000.6970.9650.1730.4610.924
mobile_likes-0.0690.0300.0260.0690.4360.4190.0430.8340.6971.0000.7300.0770.1710.591
mobile_likes_received0.0060.0400.038-0.0060.5480.5100.0060.7840.9650.7301.0000.1620.3760.826
tenure0.3410.0530.036-0.3410.3090.2300.0880.1420.1730.0770.1621.0000.1910.180
www_likes0.0780.0370.025-0.0780.2730.2600.0520.5480.4610.1710.3760.1911.0000.541
www_likes_received0.0410.0440.036-0.0410.5070.4700.0050.7550.9240.5910.8260.1800.5411.000

Missing values

2025-05-18T06:26:55.705739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-18T06:26:55.948651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agedate_of_birthdob_daydob_yeardob_monthgendertenurefriend_countfriendships_initiatedlikeslikes_receivedmobile_likesmobile_likes_receivedwww_likeswww_likes_received
0141999-11-1919199911male266.000000000
1141999-11-022199911female6.000000000
2141999-11-1616199911male13.000000000
3141999-12-2525199912female93.000000000
4141999-12-044199912male82.000000000
5141999-12-011199912male15.000000000
6132000-01-141420001male12.000000000
7132000-01-04420001female0.000000000
8132000-01-01120001male81.000000000
9132000-02-02220002male171.000000000
agedate_of_birthdob_daydob_yeardob_monthgendertenurefriend_countfriendships_initiatedlikeslikes_receivedmobile_likesmobile_likes_receivedwww_likeswww_likes_received
98993191994-08-151519948male394.04538414445011508844355961669127
98994201993-01-04419931female402.01988332735110602572487333310332692
98995201993-10-099199310female699.03611973450777684414690993859
98996241989-04-252519894female182.0293812726018177655843117081756057
98997281985-12-1414198512female290.022181618462610268429042503366018
98998681945-04-04419454female541.021183413996180893505118874916202
98999181995-03-121219953female21.01968172044011341243991059222820
99000151998-05-101019985female111.0200215241195912554119591146201092
99001231990-04-111119904female416.0256018545066516450657600756
99002391974-05-151519745female397.020497689410124439410953002913

Duplicate rows

Most frequently occurring

agedate_of_birthdob_daydob_yeardob_monthgendertenurefriend_countfriendships_initiatedlikeslikes_receivedmobile_likesmobile_likes_receivedwww_likeswww_likes_received# duplicates
2251988-01-01119881male21.0000000004
0231990-01-01119901male507.0110000002
1251988-01-01119881male0.0000000002
3251988-01-01119881male33.0000000002
4261987-01-01119871male500.0220000002
5331980-01-01119801male17.0000000002